Browsing by Author "Shohan, Yeasir Arafat"

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Addressing Agricultural Challenges: An Identification of Best Feature Selection Technique for Dragon Fruit Disease Recognition
(Elsevier, 2023-11-02) Shakil, Rashiduzzaman; Islam, Shawn; Shohan, Yeasir Arafat; Mia, Anonto; Rajbongshi, Aditya; Rahman, Md Habibur; Akter, Bonna
Dragon fruit is a prominent substance in global agriculture. Despite this, it is gaining popularity and is a viable solution in resource-poor, environmentally degraded areas because of its many health benefits. Nevertheless, many dragon fruit plantations have been impacted by the disease, reducing their yield, and the detection system is still conventional. Farmers’ lack of disease identification and management expertise diminished crop quality and products. As a result, little research was carried out to assist those specific farmers requiring adequate agricultural support. This research has proposed an autonomous agro-based system to recognize dragon diseases using in-depth analysis of feature selection techniques. After the collection of real-time images of the dragon, the images are preprocessed using various image-processing techniques. The two important features are retrieved after segmentation. The analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) are used as feature selection techniques to assess the feature rank based on the mutual score. To analyze the effectiveness of the machine learning algorithms that were used, six distinct machine learning classifiers were applied to the top-ranked feature sets, and their performance was measured using seven distinct performance evaluation metrics. AdaBoost and Random Forest classifiers for the LASSO feature ranking approach got the maximum accuracy, which is 96.29%, based on a comparison of classifiers based on the ANOVA and LASSO feature set. Despite this, we have optimized the computational resources of each classifier for the LASSO feature set.
Productivity Analysis of Potato (Solanum tuberosum) Yielding in Cumilla, Bangladesh Based on Soil Chemical Parameters Using Machine Learning Approaches.
(DAFFODIL INTERNATIONAL UNIVERSITY, 2024-09-24) Hasan, Md. Kamrul; Mia, Anonto; Shohan, Yeasir Arafat
The aim of this project is to apply machine learning methods to evaluate the productivity of potato yields in Cumilla, Bangladesh, based on soil chemical parameters. Collaborating with the Regional Agricultural Research Station, Bangladesh Agriculture Research Institute (BARI) in Cumilla, a comprehensive dataset spanning crop yields, soil quality, climate conditions, and region-specific agronomic practices has been developed. This dataset was subjected to 277 machine learning classifiers in order to determine the most effective techniques for predicting potato productivity. Based to the analysis, the top 25 classifiers including AdaBoost, GLMBoost, CNN, Simulated Annealing, Bayesian Optimization, GAN, Multi-Layer Perceptron, Support Vector Machine, FDA, Deep-Q-Network, Linear Regression, LDA, Ensemble Model, Autoencoder provided significant insights into the parameters influencing potato yield, with soil chemical characteristics emerging as key influences. Simulated Annealing, Bayesian Optimization, MLP, and CNN exhibit outstanding results with 90%, 89%, 88%, and 87% accuracy, respectively. despite this, GAN approaches and become the best match for the dataset with 99.96% accuracy. The correctness and applicability of the dataset to the regional agricultural environment were validated during the validation procedure. The relationship between crop yield and soil properties has become clearer because of to these discoveries, which have real-world implications for enhancing agricultural practices in Bangladesh. The verified dataset supports data-driven decision-making and sustainable development objectives by bridging a critical gap in the local agricultural research infrastructure